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1.6.0-DEV-03e1a89ff1.log
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Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
Resolving package versions...
[ Info: LEGAL NOTICE: package operations send anonymous data about your system to https://pkg.julialang.org (your current package server), including the operating system and Julia versions you are using, and a random client UUID. Running `Pkg.telemetryinfo()` will show exactly what data is sent. See https://julialang.org/legal/data/ for more details about what this data is used for, how long it is retained, and how to opt out of sending it.
Installed InvertedIndices ────────────── v1.0.0
Installed PDMats ─────────────────────── v0.10.0
Installed IteratorInterfaceExtensions ── v1.0.0
Installed Rmath ──────────────────────── v0.6.1
Installed FilePathsBase ──────────────── v0.8.0
Installed Parsers ────────────────────── v1.0.10
Installed DataFrames ─────────────────── v0.21.6
Installed JWAS ───────────────────────── v0.9.0
Installed FillArrays ─────────────────── v0.9.2
Installed WeakRefStrings ─────────────── v0.6.2
Installed StatsFuns ──────────────────── v0.9.5
Installed DataAPI ────────────────────── v1.3.0
Installed Rmath_jll ──────────────────── v0.2.2+1
Installed ProgressMeter ──────────────── v1.3.2
Installed OpenSpecFun_jll ────────────── v0.5.3+3
Installed CategoricalArrays ──────────── v0.8.1
Installed Missings ───────────────────── v0.4.3
Installed DataValueInterfaces ────────── v1.0.0
Installed CompilerSupportLibraries_jll ─ v0.3.3+0
Installed SpecialFunctions ───────────── v0.10.3
Installed OrderedCollections ─────────── v1.3.0
Installed Reexport ───────────────────── v0.2.0
Installed Compat ─────────────────────── v3.13.0
Installed Tables ─────────────────────── v1.0.5
Installed QuadGK ─────────────────────── v2.4.0
Installed TableTraits ────────────────── v1.0.0
Installed JSON ───────────────────────── v0.21.0
Installed PooledArrays ───────────────── v0.5.3
Installed StatsBase ──────────────────── v0.33.0
Installed Distributions ──────────────── v0.23.9
Installed SortingAlgorithms ──────────── v0.3.1
Installed DataStructures ─────────────── v0.17.20
Installed CSV ────────────────────────── v0.6.2
Updating `~/.julia/environments/v1.6/Project.toml`
[c9a035f4] + JWAS v0.9.0
Updating `~/.julia/environments/v1.6/Manifest.toml`
[336ed68f] + CSV v0.6.2
[324d7699] + CategoricalArrays v0.8.1
[34da2185] + Compat v3.13.0
[e66e0078] + CompilerSupportLibraries_jll v0.3.3+0
[9a962f9c] + DataAPI v1.3.0
[a93c6f00] + DataFrames v0.21.6
[864edb3b] + DataStructures v0.17.20
[e2d170a0] + DataValueInterfaces v1.0.0
[31c24e10] + Distributions v0.23.9
[48062228] + FilePathsBase v0.8.0
[1a297f60] + FillArrays v0.9.2
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[c9a035f4] + JWAS v0.9.0
[e1d29d7a] + Missings v0.4.3
[efe28fd5] + OpenSpecFun_jll v0.5.3+3
[bac558e1] + OrderedCollections v1.3.0
[90014a1f] + PDMats v0.10.0
[69de0a69] + Parsers v1.0.10
[2dfb63ee] + PooledArrays v0.5.3
[92933f4c] + ProgressMeter v1.3.2
[1fd47b50] + QuadGK v2.4.0
[189a3867] + Reexport v0.2.0
[79098fc4] + Rmath v0.6.1
[f50d1b31] + Rmath_jll v0.2.2+1
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.10.3
[2913bbd2] + StatsBase v0.33.0
[4c63d2b9] + StatsFuns v0.9.5
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v1.0.5
[ea10d353] + WeakRefStrings v0.6.2
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Testing JWAS
Status `/tmp/jl_n5iUHf/Project.toml`
[336ed68f] CSV v0.6.2
[a93c6f00] DataFrames v0.21.6
[31c24e10] Distributions v0.23.9
[c9a035f4] JWAS v0.9.0
[92933f4c] ProgressMeter v1.3.2
[2913bbd2] StatsBase v0.33.0
[8bb1440f] DelimitedFiles
[b77e0a4c] InteractiveUtils
[37e2e46d] LinearAlgebra
[de0858da] Printf
[9a3f8284] Random
[2f01184e] SparseArrays
[8dfed614] Test
Status `/tmp/jl_n5iUHf/Manifest.toml`
[336ed68f] CSV v0.6.2
[324d7699] CategoricalArrays v0.8.1
[34da2185] Compat v3.13.0
[e66e0078] CompilerSupportLibraries_jll v0.3.3+0
[9a962f9c] DataAPI v1.3.0
[a93c6f00] DataFrames v0.21.6
[864edb3b] DataStructures v0.17.20
[e2d170a0] DataValueInterfaces v1.0.0
[31c24e10] Distributions v0.23.9
[48062228] FilePathsBase v0.8.0
[1a297f60] FillArrays v0.9.2
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[c9a035f4] JWAS v0.9.0
[e1d29d7a] Missings v0.4.3
[efe28fd5] OpenSpecFun_jll v0.5.3+3
[bac558e1] OrderedCollections v1.3.0
[90014a1f] PDMats v0.10.0
[69de0a69] Parsers v1.0.10
[2dfb63ee] PooledArrays v0.5.3
[92933f4c] ProgressMeter v1.3.2
[1fd47b50] QuadGK v2.4.0
[189a3867] Reexport v0.2.0
[79098fc4] Rmath v0.6.1
[f50d1b31] Rmath_jll v0.2.2+1
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.10.3
[2913bbd2] StatsBase v0.33.0
[4c63d2b9] StatsFuns v0.9.5
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v1.0.5
[ea10d353] WeakRefStrings v0.6.2
[2a0f44e3] Base64
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[9fa8497b] Future
[b77e0a4c] InteractiveUtils
[76f85450] LibGit2
[8f399da3] Libdl
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[d6f4376e] Markdown
[a63ad114] Mmap
[44cfe95a] Pkg
[de0858da] Printf
[3fa0cd96] REPL
[9a3f8284] Random
[ea8e919c] SHA
[9e88b42a] Serialization
[1a1011a3] SharedArrays
[6462fe0b] Sockets
[2f01184e] SparseArrays
[10745b16] Statistics
[4607b0f0] SuiteSparse
[8dfed614] Test
[cf7118a7] UUIDs
[4ec0a83e] Unicode
The delimiter in pedigree.txt is ','.
coding pedigree... 8%|██▋ | ETA: 0:00:06[Kcoding pedigree... 100%|████████████████████████████████| Time: 0:00:00[K
calculating inbreeding... 8%|██▏ | ETA: 0:00:03[Kcalculating inbreeding... 100%|█████████████████████████| Time: 0:00:00[K
Pedigree informatin:
#individuals: 12
#sires: 4
#dams: 5
#founders: 3
Test single-trait GBLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0]
random effect variances (y1:x2): [1.0]
genetic variances (polygenic):
1.0 0.5
0.5 1.0
residual variances: 1.000
Genomic Information:
complete genomic data (i.e., non-single-step analysis)
Genomic Category geno
Method GBLUP
genetic variances (genomic): 1.000
estimateScale false
Degree of freedom for hyper-parameters:
residual variances: 4.000
random effect variances: 5.000
polygenic effect variances: 6.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1.
The file MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno.
The file MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno.
The file MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances.
The file MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:07:16[Krunning MCMC for BayesC... 2%|▌ | ETA: 0:04:11[Krunning MCMC for BayesC... 10%|██▌ | ETA: 0:01:20[K
Posterior means at iteration: 50
Residual variance: 1.776983
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:09[K
Posterior means at iteration: 100
Residual variance: 1.293065
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:09[K
The version of Julia and Platform in use:
Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
JULIA_PKGEVAL = true
JULIA_LOAD_PATH = @:/tmp/jl_n5iUHf
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait GBLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
dam is not found in model equation 2.
dam is not found in model equation 3.
x2 is not found in model equation 1.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam + geno
y2 = intercept + x1 + x2 + x3 + ID + geno
y3 = intercept + x1 + x1*x3 + x2 + ID + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y2:ID,y3:ID,y1:dam):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
random effect variances (y2:x2,y3:x2):
1.0 0.5
0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
residual variances:
1.0f0 0.5f0 0.5f0
0.5f0 1.0f0 0.5f0
0.5f0 0.5f0 1.0f0
Genomic Information:
complete genomic data (i.e., non-single-step analysis)
Genomic Category geno
Method GBLUP
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
estimateScale false
Degree of freedom for hyper-parameters:
residual variances: 7.000
random effect variances: 6.000
polygenic effect variances: 8.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1.
The file MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2.
The file MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3.
The file MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno.
The file MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno.
The file MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances.
The file MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 1%| | ETA: 0:14:38[Krunning MCMC for conventional (no markers)... 2%|▏ | ETA: 0:08:00[Krunning MCMC for conventional (no markers)... 10%|▋ | ETA: 0:01:45[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.807337 0.265465 -0.043654; 0.265465 0.523732 0.124284; -0.043654 0.124284 0.626463]
running MCMC for conventional (no markers)... 50%|███ | ETA: 0:00:12[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.764075 0.496846 0.267045; 0.496846 1.174522 0.426034; 0.267045 0.426034 0.894128]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:12[K
The version of Julia and Platform in use:
Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
JULIA_PKGEVAL = true
JULIA_LOAD_PATH = @:/tmp/jl_n5iUHf
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test single-trait BayesA analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0]
random effect variances (y1:x2): [1.0]
genetic variances (polygenic):
1.0 0.5
0.5 1.0
residual variances: 1.000
Genomic Information:
complete genomic data (i.e., non-single-step analysis)
Genomic Category geno
Method BayesB
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
estimatePi false
estimateScale false
Degree of freedom for hyper-parameters:
residual variances: 4.000
random effect variances: 5.000
polygenic effect variances: 6.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1.
The file MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno.
The file MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno.
The file MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances.
The file MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:00:57[Krunning MCMC for BayesC... 2%|▌ | ETA: 0:00:42[K
Posterior means at iteration: 50
Residual variance: 0.815999
Posterior means at iteration: 100
Residual variance: 0.714339
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
JULIA_PKGEVAL = true
JULIA_LOAD_PATH = @:/tmp/jl_n5iUHf
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
(3×13 DataFrame
│ Row │ trait │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │ estimateGenVar │ stdGenVar │ prGenVar │ WPPA │ PPA_t │
│ │ Int64 │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┼────────────────┼───────────┼──────────┼─────────┼─────────┤
│ 1 │ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │ 0.345201 │ 0.446668 │ 26.85 │ 1.0 │ 1.0 │
│ 2 │ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │ 1.43501 │ 1.81414 │ 44.2 │ 1.0 │ 1.0 │
│ 3 │ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │ 0.428704 │ 0.68718 │ 15.82 │ 1.0 │ 1.0 │,)
Test multi-trait BayesA analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
dam is not found in model equation 2.
dam is not found in model equation 3.
x2 is not found in model equation 1.
BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam + geno
y2 = intercept + x1 + x2 + x3 + ID + geno
y3 = intercept + x1 + x1*x3 + x2 + ID + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y2:ID,y3:ID,y1:dam):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
random effect variances (y2:x2,y3:x2):
1.0 0.5
0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
residual variances:
1.0f0 0.5f0 0.5f0
0.5f0 1.0f0 0.5f0
0.5f0 0.5f0 1.0f0
Genomic Information:
complete genomic data (i.e., non-single-step analysis)
Genomic Category geno
Method BayesB
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["N", "Y", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["Y", "N", "Y"] 0.0
["N", "Y", "N"] 0.0
["Y", "Y", "Y"] 1.0
estimatePi false
estimateScale false
Degree of freedom for hyper-parameters:
residual variances: 7.000
random effect variances: 6.000
polygenic effect variances: 8.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1.
The file MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2.
The file MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3.
The file MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno.
The file MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno.
The file MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances.
The file MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 1%| | ETA: 0:05:23[Krunning MCMC for conventional (no markers)... 2%|▏ | ETA: 0:03:42[Krunning MCMC for conventional (no markers)... 37%|██▎ | ETA: 0:00:08[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.297325 0.056189 0.447262; 0.056189 0.80686 0.596732; 0.447262 0.596732 0.984449]
running MCMC for conventional (no markers)... 91%|█████▌| ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.427163 0.411622 0.76521; 0.411622 0.883295 0.648271; 0.76521 0.648271 1.08779]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
JULIA_PKGEVAL = true
JULIA_LOAD_PATH = @:/tmp/jl_n5iUHf
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
(3×13 DataFrame
│ Row │ trait │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │ estimateGenVar │ stdGenVar │ prGenVar │ WPPA │ PPA_t │
│ │ Int64 │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┼────────────────┼───────────┼──────────┼─────────┼─────────┤
│ 1 │ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │ 0.347573 │ 0.433727 │ 41.8 │ 1.0 │ 1.0 │
│ 2 │ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │ 0.332296 │ 0.405152 │ 29.56 │ 1.0 │ 1.0 │
│ 3 │ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │ 0.569409 │ 0.572293 │ 51.14 │ 1.0 │ 1.0 │,)
Test single-trait BayesC analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0]
random effect variances (y1:x2): [1.0]
genetic variances (polygenic):
1.0 0.5
0.5 1.0
residual variances: 1.000
Genomic Information:
complete genomic data (i.e., non-single-step analysis)
Genomic Category geno
Method BayesC
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
estimatePi true
estimateScale false
Degree of freedom for hyper-parameters:
residual variances: 4.000
random effect variances: 5.000
polygenic effect variances: 6.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1.
The file MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno.
The file MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno.
The file MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances.
The file MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 31%|███████▊ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual variance: 2.087776
Posterior means at iteration: 100
Residual variance: 1.649022
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.6.0-DEV.633
Commit 03e1a89ff1 (2020-08-13 14:37 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake-avx512)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
JULIA_NUM_THREADS = 2
JULIA_PKGEVAL = true
JULIA_LOAD_PATH = @:/tmp/jl_n5iUHf
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.7 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.2 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
(3×13 DataFrame
│ Row │ trait │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │ estimateGenVar │ stdGenVar │ prGenVar │ WPPA │ PPA_t │
│ │ Int64 │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼───────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┼────────────────┼───────────┼──────────┼─────────┼─────────┤
│ 1 │ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │ 0.108055 │ 0.152439 │ 62.66 │ 0.7 │ 0.7 │
│ 2 │ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │ 0.390083 │ 0.665247 │ 51.73 │ 0.6 │ 0.65 │
│ 3 │ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │ 0.0582369 │ 0.0978581 │ 48.91 │ 0.5 │ 0.6 │,)
Test multi-trait BayesC analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
#markers: 5; #individuals: 7
dam is not found in model equation 2.
dam is not found in model equation 3.
x2 is not found in model equation 1.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 5 are missing. This record is deleted.
The number of observations with both genotypes and phenotypes used in the analysis is 4.
The number of observations with both phenotype and pedigree information used in the analysis is 4.
Missing values are found in independent variables: dam.
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam + geno
y2 = intercept + x1 + x2 + x3 + ID + geno
y3 = intercept + x1 + x1*x3 + x2 + ID + geno
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
chain_length 100
burnin 0
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed 314
Hyper-parameters Information:
random effect variances (y1:ID,y2:ID,y3:ID,y1:dam):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
random effect variances (y2:x2,y3:x2):
1.0 0.5
0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
residual variances:
1.0f0 0.5f0 0.5f0
0.5f0 1.0f0 0.5f0
0.5f0 0.5f0 1.0f0